Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2(nd) phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

Retrospective Study and Evaluation of School Failure (Junior High School) in Scientific Subjects

This article aims, on the one hand, to theoretically analyze the fact of school failure by identifying and describing its extent, specifically in the province of Ouezzane in northern Morocco, on the other hand, it aims to describe the effect of hybrid education caused by the COVID-19 health crisis on student results in the 2020/2021 school year as well as to make a comparative analysis of school failure rates following an exploratory approach for previous school years;namely, the years 2015/2016, 2016/2017, 2017/2018 and 2018/2019. In order to carry out this study, we proceeded with an in-depth analysis of the marks of the students relating to the scientific subjects, in particular: mathematical sciences, sciences of life and earth and physics, resulting from the school curriculum obtained at the regional examination for the third year of college. Finally, we have suggested some recommendations regarding the technology plan that aim to reduce the rate of this failure in this province in particular and can be generalized in the other parts of the kingdom. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Analyzing Academic Results of Managerial Accounting Courses Using Offline and Online Learning at Undergraduate Study Program

This study aims to analyze the comparison of the academic result of the EA447 Managerial Accounting course using offline and online learning at the Management and Accounting undergraduate study program during 10 semesters of the 2016-2020 academic years at Indonesia Persada University-Jakarta. This study uses simple descriptive statistics to analyze the pass rate, failure rate and eligibility rate to take the final semester exam. The data is taken from the academic section of the institution in the form of student scores based on offline learning before the covid-19 pandemic, namely the odd semester 2016/17, 2017/18, 2018/19 and 2019/20, and 2019/20 and the academic year in online learning during the covid19 pandemic in the even semester 2019/2020, and the 2020/2021 school year. The results of this study indicate that the graduation rate is high, the failure rate is low, and the eligibility rate for taking the final semester exam is lower in online learning outcomes than offline learning outcomes. This finding implied to provide references in applying offline and online learning for the Management and Accounting undergraduate study program at the university. © 2022 Association for Computing Machinery.

Supplemental Instruction to Decrease Equity Gaps in GateKeeper Engineering Courses (ERM)

This study explores the impact of Technology-Assisted Supplemental Instruction (TASI) on the sense of belonging and academic achievement of URM identified students in Statics courses at a large public HSI university. TASI is a peer-led tutoring service in partnership with faculty support that targets high failure rate STEM courses, in this case, three different iterations of Statics. Students completed four surveys that measured demographics, sense of belonging in their field of study, and confidence in their ability to do well in their courses. In addition, TASI attendance, students' academic and enrollment data were collected. Preliminary belonging data at the beginning of the term showed the nearly 80% of Latinx students agreed with the statements: “I sometimes feel like other students in my field of study have skills that I do not,” and “When I struggle in a class I feel that I don't belong in the field”. Linear regression also shows that the main predictor of student grades in Statics are identifying as a URM student or Pell recipient. TASI has the goal of increasing academic support and therefore performance to alleviate these feelings and ensure student persistence. Using matched pairs analysis, the data shows a statistically significant increase of 0.4 to 0.5 in course grade on a 4-point scale. These results were most apparent in URM students. The rate of failing grades for URM students decreased up to twenty percent (depending on the section). The impact of the TASI is more evident for students of color during the COVID pandemic and virtual learning. The use of an anti-deficit lens highlights how imperative it is to have meaningful, useful, and accessible interventions. Student facilitators, access, and awareness of programs are noted as crucial to success. © American Society for Engineering Education, 2022.

Revisualizing Statics

This evidence-based practice paper describes the study of Statics or Engineering Mechanics 1 course. This is an entry-level course for freshman or sophomores in many engineering disciplines and includes topics such as forces, equilibrium of forces, truss analysis, centroid, and moment of inertia. It is observed that this is a difficult course for students and the passing rate is 60.7% [1]. To reduce the failure rate of students in class, instructors often try to implement a variety of methods. Hands-on models, active learning methods, ExCEEd model [2], Flipped Classroom model [3], and Montessori Based Engineering (MBE) Model [4] are some of the models used by instructors. Student success improved by 3%-7% [5] when these methodologies were used. Hands-on models when used in combination with other active learning methods are engaging and useful in maintaining student interest [2]. In-class instruction is usually expected for active learning using hands-on models for Statics 1. This has been especially difficult during COVID-restricted learning situations and has created a learning gap between current students and former. This paper describes the use of Virtual Reality (VR), a computer-generated simulation of a three-dimensional object or space, to fill both of these issues caused by remote learning.VR is a powerful flexible platform that when utilized can generate differentiating perspectives of problems. The VR tool Tilt Brush [6] was used to replace the physical hands-on models typically used in class to provide an engaging diverse experience. Typically, the students are introduced to a 3D Vector problem as a picture in a book or PowerPoint slide and the instructor proceeds to explain the problem using direct instruction. This method can work for some students. However, students might not be entirely clear on how the vectors and angles are actually represented in 3D space. Instructors have tried to use physical hands-on models to help students understand this concept. Most students develop an understanding of visualizing and analyzing 3D vectors after working with these physical models, thus it is great for in-person learning. However, this is not possible in an online environment. There is no denying that higher education is moving toward online learning [7]. COVID brought to everyone's attention how educators need to better prepare to transition into an online learning environment. With this in mind, the authors decided to create "hands-on models" in Virtual Reality. These models were presented in different formats in order to provide a variety of perspectives and to help engage the students in the learning module. Student engagement was very high for this module when students were shown videos. If it was made more hands-on by teaching synchronously, it is expected that student engagement would be even higher. Development of teaching this 3D Vector module, student assessment, survey, and conclusions are included in this paper. The goal of this paper is to inspire and encourage a Statics 1 instructor to start using VR in their own course and then possibly consider expanding the VR technique to other mechanics concepts and major courses. © American Society for Engineering Education, 2022.

ON THE CHARACTERISATION OF X-LINDLEY DISTRIBUTION BY TRUNCATED MOMENTS. PROPERTIES AND APPLICATION

This paper presents the characterisation of X-Lindley distribution using the relation between truncated moment and failure rate/reverse failure rate function. An application of this distribution to real data of survival times (in days) of 92 Algerian individuals infected with coronavirus is given. © 2022 by the authors.

Pharmaceutical plant machine availability prediction using Artificial Neural Network

During the 2nd phase of COVID-19 pandemic, pharmaceutical plant industry is facing lot of production pressure and machine availability plays vital role in maximizing the manufacturing pharmacy product output. In this paper, Artificial Neural Networks (ANNs) based information processing algorithm has been used to provide a solution to this problem and it has been found suitable to predict machines availability as a prediction function. The considered pharmaceutical plants are dealing with production of medicines related common symptoms in case of COVID-19 (fever, coughing, and breathing problems). The pharmaceutical plant data corresponding to different values of repair and failure rates of different subsystems is collected from plant and analyzed with the help of validated neural network value of availability. This configuration of ANNs approach developed in this research allowed simplifying computational complexities of conventional approaches to solve a large plant machines availability problem. The ANNs methodology in the paper permitted making no assumption, no explicit coding of the problem, no complete knowledge of system configuration, only raw input and clean data found to be sufficient to determine the value of machine availability function for different value of failure and repair rates considered in the paper. The results obtained in the paper are useful for the plant leadership, as the value of failure and repair rates of various subsystems can be fine-tuned at a require clear-cut level to achieve higher availability, and avoid considerably loss of production, loss of man power, and by-pass complete breakdown of concerned system.

Comparative study of artificial neural network versus parametric method in COVID-19 data analysis.

Since the previous two years, a new coronavirus (COVID-19) has found a major global problem. The speedy pathogen over the globe was followed by a shockingly large number of afflicted people and a gradual increase in the number of deaths. If the survival analysis of active individuals can be predicted, it will help to contain the epidemic significantly in any area. In medical diagnosis, prognosis and survival analysis, neural networks have been found to be as successful as general nonlinear models. In this study, a real application has been developed for estimating the COVID-19 mortality rates in Italy by using two different methods, artificial neural network modeling and maximum likelihood estimation. The predictions obtained from the multilayer artificial neural network model developed with 9 neurons in the hidden layer were compared with the numerical results. The maximum deviation calculated for the artificial neural network model was -0.14% and the R value was 0.99836. The study findings confirmed that the two different statistical models that were developed had high reliability.

Proactive Operation Strategy to enhance resiliency of Indian Power System during Super Cyclone Amphan

Power systems are designed to be operated under expected weather conditions. Unexpected weather conditions sometimes create widespread damage to the power system. The failure rate of equipment in power system along with redundancy and accuracy of forecasting decide the scope of damage. Eastern coast of Indian sub-continent experiences cyclones of varying intensity every year. These cyclones have severe impact on the infrastructure including the power infrastructure. The proactive operation strategy to counter each stage of uncertainty helps not only in managing the power system but also in early restoration. Amid the NCOVID-19 pandemic, Indian power system witnessed super cyclone named 'AMPHAN' which originated in the Bay of Bengal. The cyclone, after landfall passed through densely populated regions affecting the load centers. Cyclone of matching severity was last witnessed by India in the year 1999. The landfall of cyclone started during afternoon hours of 20th May 2020. The proactive action strategy based on past experience resulted in minimization of loss to electrical system and power supply outage. This paper presents the proactive measures taken by POSOCO and power utilities across all key sectors, viz., generation, transmission, distribution, during different phases of cyclone trajectory and the impact of cyclone on Indian power system. © 2021 IEEE.

Some new findings on the survival Rényi entropy and application of COVID-19 data.

Motivated by the connotation of survival Rényi entropy and its related dynamic version, we introduce them in terms of their lower bounds and mean residual life function. Moreover, we illustrate the relation between survival Rényi entropy and some of measures of information. Furthermore, the hazard rate order implies ordering of dynamic survival Rényi entropy. Our models are considered a more comprehensive version of generalized order statistics and give some properties and characterization results. Finally, a non-parametric estimation of survival Rényi entropy is included based on real COVID-19 data and simulated data.

Proposing a Conceptual Model for Cloud Computing Adoption in the Libyan E-Government

The Race towards digital transformation, many developed countries are prone to integrate cloud technology for improving their E-government services in delivering automated and efficient services to their citizens, especially in the critical period of Covid-19 pandemic. But developing countries are lagging in the presence of high failure rate of e-government projects and Libya is one of them which is unable to deliver efficient e-government services to all citizens. To fill this research gap, the current study proposes a model based on Trust theory and the Technology Organization Environment (TOE) framework for investigating the key factors in technology, environment, organization and trust perspectives. This is a position study that presents ongoing research. The study provides guidelines to adopt the cloud computing in Libyan government that would enhance the e-government services in terms of accessibility, efficacy and transparency as well as allowing overall public engagement in entire government procedures. © 2021 IEEE.